In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling.
About this Course
High school algebra
Skills you will gain
High school algebra
University of Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
- 5 stars76.08%
- 4 stars18.53%
- 3 stars3.62%
- 2 stars0.85%
- 1 star0.89%
TOP REVIEWS FROM UNDERSTANDING AND VISUALIZING DATA WITH PYTHON
Excellent course materials, especially the videos, with content that is thoughtfully composed and carefully edited. Very good python training, great instructors, and overall great learning experience.
Good content but I dont like some assigment/assessment, especially the one asking to write a memorandum, which is totally not related to "Understanding and Visualizing Data with Python"
This was a quick way of understanding the basics. I liked how detailed and basic the learning instructions were. Anyone, even those without a statistics background can begin from here
Really enjoyed this course. Looking forward to the next part of the specialization. I thought the quality of the lectures was excellent and made the topic interesting and digestible
About the Statistics with Python Specialization
This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.
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